Estimation and tracking of the instantaneous amplitudes and frequencies of superimposed, slowly varying narrowband signals is a difficult signal processing problem that shows up in many applications. Our approach to this problem is to achieve global noise averaging via SVD-based rank reduction of a matrix constructed from the entire data record. Compared to methods that use local noise averaging using many smaller matrices, the strength of the new approach is in affecting better noise reduction at a lower computational cost. Moreover, no model is needed for the variation of the amplitudes and frequencies with time. In this paper, Cramer-Rao bounds for the variance of the error in tracking the instantaneous amplitudes and frequencies (without assuming a parametric model for their variation with time) will be presented. In addition, we will also show how the algorithm can be used on sensor array data to estimate range and direction of multiple narrow-band sources.